What are some tactics for recognizing artificially made media?
Digital Media Forensics (DMF) field aims to develop technologies for the automated assessment of the integrity of an image or video, so DMF is the field you are looking for. There are several approaches in DMF: for example, those based on machine learning (ML) techniques, in particular, convolutional neural networks (CNNs). 

For example, in the paper Deepfake Video Detection Using Recurrent Neural Networks (2018), David Güera and Edward J. Delp propose a two-stage analysis composed of a CNN to extract features at the frame level followed by a temporally-aware RNN to capture temporal inconsistencies between frames introduced by the deepfake tool. More specifically, they use a convolutional LSTM architecture (CNN combined with an LSTM), which is trained end-to-end, so that the CNN learns the features in the videos, which are passed to the RNN, which attempts to predict the likelihood of those features belonging to a fake video or not. Section 3 explains the creation of deepfake videos, which leads to inconsistencies between video frames (which are exploited in the proposed method) because of the use of images with different viewing and illumination conditions.

Other similar works have been proposed. See this curated list https://github.com/aerophile/awesome-deepfakes for more related papers.